The invention relates to a method and to a system for the detection and localization of defects in the case of sensors in motor vehicles.
From German Patent Document DE 42 26 746 C1, a method is known for determining a driving-situation-dependent steering angle. The purpose of the method is to determine a desired steering angle value with minimal hardware expenditures. The method is based on a vehicle system model which determines a wheel steering angle based on characteristic the values of the yaw velocity and sideslip angle. The deviations of the desired values from the sensed actual values are controlled by means of a state controlling system.
In such so-called xe2x80x9cX-by-wirexe2x80x9d systems, electronic vehicle components actively intervene in the driving event and the driving dynamics. This control or automatic control is based on the sensed values. Hence, a defective sensor results in an incorrect actual value for the corresponding quantity. This has the result that the control signals generated by the control or automatic control which influence the driving dynamics are generated on the basis of incorrect information, which may cause an undesired reaction of the vehicle and an impairment of vehicle handling.
The invention is based on the problem of clearly detecting and localizing defects in sensors for detecting system quantities which are used for describing the state of the motor vehicle.
According to the invention, this problem is solved by a method and system for detecting and localizing sensor defects in motor vehicles, in which a measuring signal (yk) for the description of the dynamic behavior of the motor vehicle is determined by a sensor; from a mathematical equivalent model, a state value (xxe2x88x92k) is computed which is assigned to the measuring signal (yk); an estimation error (exe2x88x92k) of the state value (xxe2x88x92k) is determined; a residue (rk) from the difference between the measuring signal (yk) and a reference quantity (C*xxe2x88x92k) corresponding to the measuring signal (yk) and formed from the state value (xxe2x88x92k) is determined; a characteristic parameter (xcex5) is formed from the multiplication of the residue (rk) and the estimation error (exe2x88x92k); and an error signal (EI, EL) is generated when the characteristic parameter (xcex5) exceeds a limit value (sk). The system has a computing unit for determining the state values (xxe2x88x92k) and the residues (rk) describing the state of the motor vehicle, the measuring signals (yk) of the sensors being feedable as input signals to the computing unit, and an evaluation unit connected on the output side of the computing unit, to which evaluation unit the state values (xxe2x88x92k) and residues (rk) can be fed as input signals and in which characteristic parameters (xcex5) can be generated from the input signals and can be compared with limit values (sk). An error signal (EI, EL) is produced in the evaluation unit if the characteristic parameters (xcex5) exceed the limit values (sk).
The measuring signals, which are received by sensors, flow into a mathematical equivalent model in which state quantities are estimated which describe the dynamic behavior of the vehicle. These calculated state quantities have an estimation error whose quantity is determined and is multiplied by a residue which is formed from the difference between the measuring signal and a reference quantity which corresponds to the measuring signal and is calculated from the state quantities of the mathematical equivalent model. From the multiplication of the estimation error with the residue, a characteristic parameter is generated by which sensor defects can be detected and localized. The residue is amplified by multiplication with the estimation error, whereby problems with an amplitude which is too low and with superimposed system noise and measuring noise are avoided or otherwise compensated.
The characteristic parameter is used for a further analysis in that a defect signal is generated if the characteristic parameter exceeds a limit value. In this case, the deviation of the measuring signal from the calculated reference quantity is unacceptably high so that the characteristic parameter is above the permissible limit value and the sensor defect can clearly be discovered.
Furthermore, the defect site can also be clearly localized. When several sensors are used, a number of measuring signals is generated which corresponds to the number of sensors. Exactly one physically corresponding reference quantity is specifically assigned to the measuring signals. The reference quantity is determined from the mathematical equivalent model. A residue is assigned to each measuring signal, and a characteristic parameter is assigned to each residue so that, from the position of an unacceptably high scalar value of the parameter within the vector of all characteristic parameters, a conclusion can be drawn with respect to the incorrect measuring signal assigned to the parameter and, as a result, with respect to the defective sensor.
The mathematic equivalent model is expediently based on a Kalman filter algorithm, particularly on an expanded Kalman filter which mathematically corresponds to an observer and is capable of processing stochastic disturbances which affect the system. The sensor signals and, optionally, additional system information, are fed to the Kalman filter. From this information, prediction values, estimation filter values and residues are determined which are further processed for defect recognition. Without any physical redundancy, single as well as multiple defects, particularly in the rotational wheel speed and longitudinal acceleration sensor system, can be recognized.
The state value of the equivalent model corresponds to the prediction value of the Kalman filter which transforms by way of the measuring and observation matrix and, for generating the residues, is advantageously subtracted from the measuring signals. In a next step, from the prediction values and the residues, the optimal estimation filter values can be determined. In this case, the residues are transformed by way of an amplification matrix which preferably takes the form of a Kalman gain.
An occurring (present) sensor defect is reflected by an incorrect prediction value which differs from the estimation filter value of the preceding time period. This difference between the optimal estimation filter value and the prediction value is expressed as an estimation error which is used for identifying a defective sensor.
The estimation error is amplified by multiplication with the residues. In the case of multi-dimensional state quantities and measuring quantities, a diagonal matrix with the elements of the residue is generated on the main diagonal from the residues. The characteristic parameter determined in this manner is a typical identification characteristic which can be assigned to a certain sensor and which can be used for comparison with the permissible limit value.
Advantageously, the limit value is adaptively calculated from the weighted average value of the individual residues. Here, the residues are divided by the number of measuring signals. In addition, the individual residues can be weighted. It is thereby achieved that model uncertainties, which are the result of linearizations in the mathematical equivalent model, do not result in faulty alarms.
The rotational wheel speeds of all vehicle wheels and the longitudinal acceleration of the vehicle are preferably determined as measuring signals.
The system for detecting and localizing the sensor defects comprises a computing unit in which the mathematical equivalent model is filed, and an evaluation unit on the output side to which the state values and residues from the computing unit are fed and in which an error signal is generated if the characteristic parameter determined from the state values and the residues exceeds the limit value.
Other objects, advantages and novel features of the present invention will become apparent from the following detailed description of the invention when considered in conjunction with the accompanying drawings.